Fix UniPC dtype handling with float64 default dtype

[lindicaphxag-tech]

Validation PR for upstream issue huggingface#14888.

What does this PR do?

UniPC builds the final unit entry of rks with torch.ones((), device=device). That tensor follows the global default dtype, while the other entries follow the scheduler coefficient dtype. Under torch.set_default_dtype(torch.float64), this promotes R to float64 while b remains float32 and torch.linalg.solve(R, b) fails.

This changes both UniP and UniC paths to torch.ones_like(h), keeping the unit coefficient on the same dtype/device as the rest of the scheduler coefficients, and adds a regression test for a float64 global default.

Validation

• check_code_quality: passed
• check_repository_consistency: passed
• dependency, docs, link, missing-test, and secret checks: passed
• Dedicated GitHub-hosted validation: pytest -q tests/schedulers/test_scheduler_unipc.py passed
  • https://github.com/lindicaphxag-tech/diffusers/actions/runs/36912290662
• The fork-only failures in the stock PR workflow are infrastructure-only: upstream fast tests require Hugging Face private AWS runner groups, and size-label expects upstream labels such as size/S that do not exist in the fork.

AI-assisted contribution self-review

AI assistance was used for issue triage, implementation, and validation. I reviewed the complete diff against Diffusers' .ai/skills/self-review rubric and the testing/code-style/numerical-pitfall references.

Blocking issues: none.

Non-blocking issues: none identified.

Dead code: none. Both changes are on the active UniP/UniC scheduler update paths.

Numerical review: h has the same dtype/device provenance as the other rks entries, so ones_like(h) removes the global-default-dtype leak without changing the normal float32 path.

Verdict: READY.

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[lindicaphxag-tech]

Closing this fork-local validation PR because the upstream issue is already covered by huggingface#14920. The branch remains available for reference.